Search Results for author:
Found 9 papers, 0 papers with code
Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic
The pandemic caused by the SARS-CoV-2 virus has exposed many flaws in the decision-making strategies used to distribute resources to combat global health crises.
Reinforcement Learning for Dynamic Bidding in Truckload Markets: an Application to Large-Scale Fleet Management with Advance Commitments
Truckload brokerages, a $100 billion/year industry in the U. S., plays the critical role of matching shippers with carriers, often to move loads several days into the future.
MOLTE: a Modular Optimal Learning Testing Environment
The Matlab-based simulator allows the comparison of a number of learning policies (represented as a series of . m modules) in the context of a wide range of problems (each represented in its own . m module) which makes it easy to add new algorithms and new test problems.
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks
We also show that the knowledge gradient policy is asymptotically optimal in an offline setting.
An optimal learning method for developing personalized treatment regimes
A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals.
Finite-time Analysis for the Knowledge-Gradient Policy
We consider sequential decision problems in which we adaptively choose one of finitely many alternatives and observe a stochastic reward.
The Knowledge Gradient with Logistic Belief Models for Binary Classification
We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool and receives the binary label of the selected alternatives.
The Knowledge Gradient Policy Using A Sparse Additive Belief Model
We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power.
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable
Those similar to the current state are used to create a convex, deterministic approximation of the objective function.
